Effective classroom instruction requires monitoring student participation and interaction during class, identifying cues to simulate their attention. The ability of teachers to analyze and evaluate students’ classroom behavior is becoming a crucial criterion for quality teaching. Artificial intelligence (AI)-based behavior recognition techniques can help evaluate students’ attention and engagement during classroom sessions. With rapid digitalization, the global education system is adapting and exploring emerging technological innovations, such as AI, the Internet of Things, and big data analytics, to improve education systems. In educational institutions, modern classroom systems are supplemented with the latest technologies to make them more interactive, student centered, and customized. However, it is difficult for instructors to assess students’ interest and attention levels even with these technologies. This study harnesses modern technology to introduce an intelligent real-time vision-based classroom to monitor students’ emotions, attendance, and attention levels even when they have face masks on. We used a machine learning approach to train students’ behavior recognition models, including identifying facial expressions, to identify students’ attention/non-attention in a classroom. The attention/no-attention dataset is collected based on nine categories. The dataset is given the YOLOv5 pre-trained weights for training. For validation, the performance of various versions of the YOLOv5 model (v5m, v5n, v5l, v5s, and v5x) are compared based on different evaluation measures (precision, recall, mAP, and F1 score). Our results show that all models show promising performance with 76% average accuracy. Applying the developed model can enable instructors to visualize students’ behavior and emotional states at different levels, allowing them to appropriately manage teaching sessions by considering student-centered learning scenarios. Overall, the proposed model will enhance instructors’ performance and students at an academic level.

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students.

The You Only Look Once (YOLO) series, renowned for its efficiency and versatility in object detection, has become a fundamental component in diverse fields ranging from autonomous vehicles to robotics and video surveillance. Despite its widespread application, a notable gap exists in the literature concerning selecting YOLO models for specific tasks. Current trends often lean towards the latest models, potentially overlooking crucial factors such as computational complexity, speed, accuracy, model size, adaptability, and generalization. This approach may not always yield the optimal choice for a given application. Therefore, this paper aims to provide an exhaustive comparative analysis of various YOLO models, focusing on emotion recognition. We trained and tested YOLOv5, YOLOv7, YOLOv8, and YOLOv9 along with their respective variants, using a subset of AffectNet dataset, which consists of facial images annotated with one of five emotions, namely angry, happy, sad, neutral, and surprise. The study evaluates the models based on several key parameters: accuracy using metrics like mean Average Precision (mAP), inference time and FPS, model size, adaptability to altered datasets, and generalization capability. Comprehensive results are presented, highlighting the strengths and limitations of each model variant across these parameters. Insights are provided to guide researchers in selecting the most suitable YOLO architecture for their specific emotion recognition requirements, considering factors such as computational constraints, real-time performance needs, and the importance of accuracy vs efficiency tradeoffs. The analysis reveals the exceptional performances of certain models like YOLOv9e for high accuracy and YOLOv8n for balancing speed and accuracy. Overall, this work fills a crucial gap by offering a detailed comparative study to facilitate informed decision-making when deploying YOLO for facial emotion recognition tasks.

This article presents a scientometric and literature analysis of current research on ChatGPT, a conversational AI technology developed by OpenAI. Using various databases, 103 relevant articles were retrieved and analyzed through scientometric, quantitative, and application-based approaches. A Google trend analysis and comparison with other generative AI and chatbot technologies were also carried out. The study provides insights into the distribution of ChatGPT publications across different countries and regions, the network of co-occurring keywords, authorship analysis, article typology, and publishing entities. The findings offer a comprehensive overview of the current state of ChatGPT research, highlighting key directions for future research. The study finds that ChatGPT has gained significant attention and interest in online platforms, particularly in technology, education, and healthcare, and highlights potential ethical and legal concerns related to its use. Its applications extend to several literary and text generation areas. We do note that the sample of extracted publications is lower than anticipated due to the niche area of investigation. The article is relevant to researchers, practitioners, and policymakers interested in the field of AI-powered language models, especially ChatGPT.

Authentic classroom videos are widely used in teacher education and research because they capturecomplex behavioural and social dynamics. However, collecting and sharing such footage is costly andraises concerns about student privacy. Advances in generative AI, especially text-to-video models,now enable fully synthetic classroom videos that offer experimental control without using real studentdata. The key question is whether these videos are realistic and useful enough to serve as alternativesto authentic footage. We report findings from a survey of 128 educators who evaluated 12 short clipsfrom three sources: authentic, staged simulations, and AI-generated videos. Participants rated realism,clarity of engagement and disengagement cues, perceived strengths and limitations, and whether theywould recommend AI-generated clips for research or training. Authentic videos were rated as mostrealistic. AI-generated and staged clips were judged similarly believable but formed a lower realismtier. Educators accurately identified behavioural and emotional cues across all video types, thoughclarity varied by source and engagement state. Reported strengths of AI-generated videos includedexperimental control, efficiency, and privacy. Limitations concerned emotional depth, movementquality, and social complexity. Most respondents viewed AI-generated clips as supplements ratherthan replacements for authentic footage. These findings provide a baseline of educator perceptionsacross realism, ecological validity, social presence, perceptual authenticity, and behavioural fidelity.Based on this, we propose a Zone of Utility for synthetic media. Within this zone, current models aresuitable for controlled, privacy-safe procedural scenarios and for augmenting engagement datasets.Beyond that, limited micro-expressive fidelity makes them unsuitable for training in affective judgmentor for replacing authentic classroom recordings. The tendency to group staged and AI clips into onesynthetic category also suggests a shifting perception of artificial classroom media.